For years, merchants have sought ways to better direct their products and/or services to ultimate consumers. In advertising their products, the predicted or targeted consumer may or may not be the ultimate consumer. Further, once a purchase has been made, the merchant desires to provide follow-up products/services to create a strong customer relationship. To be successful, merchants have looked to the marketing and advertising industry for various means and methods to better communicate with their customers.
One traditional technique utilized in the marketing and advertising industry is regression analysis. Briefly, regression analysis is a statistical method for estimating cost relations. The analysis fits a regression line to data/observation points by a least squares method. Qualitatively speaking, regression analysis provides a profile of a merchant's existing customers or prospective new customers based on trackable criteria such as zip code, gender and the like.
Another example indicator utilized in the direct marketing industry is a regency frequency monetary value (RFMs). These indicators quantify how recently (regency) a purchase was made (1 month ago, 2 months ago, etc.) and how often (frequency) purchases were made in a given month. Based on these two criteria, a dollar value (called RFM value) is assigned to each pair of regency and frequency data. The RFM values are then used to make certain inferences about the consumer-purchasers.
Although various surveys, modeling analyses and indicators are available, they typically only look to profiles or groupings of types of people to predict behavior of a customer group. That is, most of the currently available analyses create simulated behavior profiles from which behavior of customers are inferred. These inferences are only fairly accurate as people often have individual (unique) desires which affect their shopping behavior. These individual desires are generally not accounted for in the prior art.